{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aABvnad-OvAs"
   },
   "source": [
    "# Step 1: Teaching an LLM how to hold a conversation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5o3OgCMdRGgp"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import torch\n",
    "from datasets import Dataset\n",
    "from transformers import (\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    BitsAndBytesConfig,\n",
    "    TrainingArguments,\n",
    "    Trainer,\n",
    "    pipeline,\n",
    "    logging,\n",
    "    DataCollatorForLanguageModeling\n",
    ")\n",
    "import json\n",
    "import pandas as pd\n",
    "from peft import LoraConfig, PeftModel, get_peft_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZQH_fuC0Ql90"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308,
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    },
    "id": "TrCGmIIqQpqk",
    "outputId": "49b90e40-24fb-43a9-8305-01a7f94d8c2f"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "14f61a3e241242ec86291e80e1f05bd6",
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      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n",
      "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
     ]
    },
    {
     "data": {
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       "model_id": "0e4b6e8cb8f048be953df358968da688",
       "version_major": 2,
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    {
     "data": {
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    },
    {
     "data": {
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       "model_id": "1190506402f54319af846ce1f6cc5ad8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "a02fdd47ceb8453398be47fc6084586c",
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     },
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     "output_type": "display_data"
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    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dac6843a7989437e817f5d921115e035",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fbb448200f7f4e48bd806161906a6bb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we are going to merge these two datasets together\n",
    "assistant_dataset = load_dataset(\"timdettmers/openassistant-guanaco\")\n",
    "\n",
    "original_guanaco_dataset = load_dataset(\"guanaco/guanaco\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cc0IYg2CQqm1",
    "outputId": "dccf471e-ab34-4293-f9f4-ffb8d918b9e8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': '### Human: Can you write a short introduction about the relevance of the term \"monopsony\" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.### Assistant: \"Monopsony\" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\\n\\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens & Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\\n\\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\\n\\nReferences:\\nBivens, J., & Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.### Human: Now explain it to a dog'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assistant_dataset['train'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-G61ZFyr-81O",
    "outputId": "d2d14b5b-0fb1-4f48-a866-dc92ffbc8793"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': \"Describe the following:\\nA tall, thin man with dark hair styled in a slick pompadour.### Response:\\nThe man is quite tall with a slender physique. His dark hair is styled in a sleek, classic pompadour with a slight sheen to it, suggesting perhaps he spends time grooming it. The hair appears to be well-maintained, as though he's put some effort into it. The dark color of his hair contrasts with his pale skin, which gives him a somewhat striking appearance. He conveys a sense of poise and confidence, as though he's comfortable in his own skin. Overall, he seems to exude a sophisticated, polished vibe.\",\n",
       " 'prompt': 'Describe the following:\\nA tall, thin man with dark hair styled in a slick pompadour.',\n",
       " 'response': \"The man is quite tall with a slender physique. His dark hair is styled in a sleek, classic pompadour with a slight sheen to it, suggesting perhaps he spends time grooming it. The hair appears to be well-maintained, as though he's put some effort into it. The dark color of his hair contrasts with his pale skin, which gives him a somewhat striking appearance. He conveys a sense of poise and confidence, as though he's comfortable in his own skin. Overall, he seems to exude a sophisticated, polished vibe.\"}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_guanaco_dataset['train'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDzsgGXa-9b9"
   },
   "source": [
    "# Parse Conversations\n",
    "\n",
    "We want to standardize each conversation into the form\n",
    "```\n",
    "[\n",
    "    ('human', 'human utterance 1'),\n",
    "    ('assistant', 'assistant utterance 1'),\n",
    "    ('human', 'human utterance 2'),\n",
    "    ('assistant', 'assistant utterance 2')\n",
    "]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "77rqBL5o9kQp",
    "outputId": "61235d9e-05e5-4fb9-cf00-45beaf9d23ae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('human', 'Can you write a short introduction about the relevance of the term \"monopsony\" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.'), ('assistant', '\"Monopsony\" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\\n\\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens & Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\\n\\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\\n\\nReferences:\\nBivens, J., & Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.'), ('human', 'Now explain it to a dog')]\n",
      "[['human', 'Describe the following:\\nA tall, thin man with dark hair styled in a slick pompadour.'], ['assistant', \"The man is quite tall with a slender physique. His dark hair is styled in a sleek, classic pompadour with a slight sheen to it, suggesting perhaps he spends time grooming it. The hair appears to be well-maintained, as though he's put some effort into it. The dark color of his hair contrasts with his pale skin, which gives him a somewhat striking appearance. He conveys a sense of poise and confidence, as though he's comfortable in his own skin. Overall, he seems to exude a sophisticated, polished vibe.\"]]\n"
     ]
    }
   ],
   "source": [
    "# Function to parse the JSON and extract human/bot conversations\n",
    "def parse_conversations(json_data):\n",
    "    if 'prompt' in json_data and 'response' in json_data:\n",
    "        return [['human', json_data['prompt']], ['assistant', json_data['response']]]\n",
    "    # Split the text on '###' to separate conversations\n",
    "    conversations = json_data['text'].split('###')\n",
    "\n",
    "    # Process each conversation and split into human and bot parts\n",
    "    convos = []\n",
    "    for convo in conversations:\n",
    "        if convo.strip():  # Check if the string is not just whitespace\n",
    "            parts = convo.strip().split(': ', 1)  # Split on the first occurrence of ': '\n",
    "            if len(parts) == 2:\n",
    "                speaker, message = parts\n",
    "                convos.append((speaker.lower(), message))\n",
    "\n",
    "    return convos\n",
    "\n",
    "# Parse the conversations\n",
    "print(parse_conversations(assistant_dataset['train'][0]))\n",
    "print(parse_conversations(original_guanaco_dataset['train'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SRX16i-58v7T",
    "outputId": "880b4f32-a0b0-4eb3-c1f7-16399ae42534"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'conversation'],\n",
       "        num_rows: 9846\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['text', 'conversation'],\n",
       "        num_rows: 518\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assistant_dataset = assistant_dataset.map(lambda x: {'conversation': parse_conversations(x)})\n",
    "assistant_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v0D2RZah8wEa",
    "outputId": "8897b491-e2c9-4095-e3f4-96a9096c0226"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'prompt', 'response', 'conversation'],\n",
       "        num_rows: 48701\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_guanaco_dataset = original_guanaco_dataset.map(lambda x: {'conversation': parse_conversations(x)})\n",
    "original_guanaco_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xr9yKFYa8wlJ",
    "outputId": "e90fa185-4b4f-4e22-f557-dd7a5531dedc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['conversation'],\n",
       "        num_rows: 46837\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['conversation'],\n",
       "        num_rows: 11710\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine the two datasets together\n",
    "dataset = Dataset.from_dict({'conversation': list(assistant_dataset['train']['conversation']) + list(original_guanaco_dataset['train']['conversation'])})\n",
    "dataset = dataset.train_test_split(test_size=0.2, seed=42)\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "peQOX_Dv8wvr"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VVUGFEXIR0fn",
    "outputId": "5b0dccd7-f73e-422f-add1-05bceda4d771"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time to define some special tokens! We aren't using Meta's standard tokens because the non chat aligned version doesn't have any\n",
    "STOP_TOKEN = '###STOP###'  # we are making these up\n",
    "HUMAN_TOKEN = '###HUMAN###'\n",
    "BOT_TOKEN = '###BOT###'\n",
    "\n",
    "EXTRA_TOKENS = {\n",
    "    'stop_token': {\n",
    "        'token': STOP_TOKEN,\n",
    "        'replace_embedding_with': 'stop talking'\n",
    "    },\n",
    "    'human_token': {\n",
    "        'token': HUMAN_TOKEN,\n",
    "        'replace_embedding_with': 'The human said:'\n",
    "    },\n",
    "    'bot_token': {\n",
    "        'token': BOT_TOKEN,\n",
    "        'replace_embedding_with': 'The assistant said:'\n",
    "    }\n",
    "}\n",
    "\n",
    "# Model from Hugging Face hub. I'm using the non chat format to show that you don't need to start from the chat checkpoint\n",
    "base_model = 'meta-llama/Meta-Llama-3-8B'\n",
    "\n",
    "# Load LLaMA tokenizer and add three special tokens\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"left\"\n",
    "\n",
    "tokenizer.add_tokens([extra['token'] for extra in EXTRA_TOKENS.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "8669fe63a4194c3382adefe214f1fefd",
      "c93a8187a8374fbba8a316b80ddf1d91",
      "6cc7ed0183834ac5b747a22fd96b3229",
      "051456e24aba4554b95215ad2cc3678b",
      "86b9e53801c54f039501d8a4e3652945",
      "b66b9bd73c664fd4865ee32f9ac1f782",
      "b2e06e4ae7ea4bf0a692019bfc46fa18",
      "d9bf868df82f48eb9142a1ee2c9115ba",
      "a3199c9ed2cb4c59a3176eb123c9a4e3",
      "12512042545d4df3969beedd302354b4",
      "b70295c80d934c4eb75912eb0a1566b0"
     ]
    },
    "id": "_ar8oDCC6BA-",
    "outputId": "8f763742-7b04-4ad0-e713-ab03dde1293d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8669fe63a4194c3382adefe214f1fefd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are going to quantize the model - lowering the precision of each parameter to make the model smaller\n",
    "#  and consume less memory\n",
    "quant_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
    "    bnb_4bit_use_double_quant=False,\n",
    ")\n",
    "\n",
    "# Load base model\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    base_model,\n",
    "    torch_dtype=torch.bfloat16,\n",
    "    quantization_config=quant_config,\n",
    "    device_map={\"\": 0}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e29KUUx7_lHM",
    "outputId": "df2364aa-79cf-4b1a-8275-b65d05927963"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128256\n",
      "128259\n"
     ]
    }
   ],
   "source": [
    "print(model.config.vocab_size)\n",
    "# resize model's embedding size to match tokenizer + 3 new special tokens\n",
    "model.resize_token_embeddings(len(tokenizer))\n",
    "print(model.config.vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "diDt77uP6Ed_",
    "outputId": "30a2044b-4922-45c8-ef0e-1d44bae3549a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replaced token \"###STOP###\" (token id 128256) weight with weight for \"stop talking\"\n",
      "Replaced token \"###HUMAN###\" (token id 128257) weight with weight for \"The human said:\"\n",
      "Replaced token \"###BOT###\" (token id 128258) weight with weight for \"The assistant said:\"\n"
     ]
    }
   ],
   "source": [
    "# We don't want to train these new embeddings (TL;DR the PEFT we are using (LoRA) cannot be used with embeddings)\n",
    "# So instead, we will set the token embeddings for our new embeddings with the average token embedding of a phrase embodying the token\n",
    "# E.g. replace ###STOP### with (Embedding(\"stop\") + Embedding(\"talking\")) / 2\n",
    "for extra_token, extra_info in EXTRA_TOKENS.items():\n",
    "    token_id = tokenizer(extra_info['token'])['input_ids'][-1]\n",
    "    new_embedding = model.model.embed_tokens.weight.data[tokenizer(extra_info['replace_embedding_with'])['input_ids'][1:]].mean(dim=0, keepdim=True)#.reshape(-1)\n",
    "    EXTRA_TOKENS[extra_token]['new_embedding'] = new_embedding\n",
    "    model.model.embed_tokens.weight.data[token_id] = EXTRA_TOKENS[extra_token]['new_embedding'].clone()\n",
    "    EXTRA_TOKENS[extra_token]['token_id'] = token_id\n",
    "    print(f\"Replaced token \\\"{extra_info['token']}\\\" (token id {token_id}) weight with weight for \\\"{extra_info['replace_embedding_with']}\\\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4szdy0Hg7e8O",
    "outputId": "8574bf48-7905-486c-98f1-076b199bd376"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'stop_token': {'token': '###STOP###',\n",
       "  'replace_embedding_with': 'stop talking',\n",
       "  'new_embedding': tensor([[-0.0198, -0.0090,  0.0052,  ...,  0.0225,  0.0108,  0.0082]],\n",
       "         device='cuda:0', dtype=torch.bfloat16),\n",
       "  'token_id': 128256},\n",
       " 'human_token': {'token': '###HUMAN###',\n",
       "  'replace_embedding_with': 'The human said:',\n",
       "  'new_embedding': tensor([[-0.0041, -0.0070, -0.0053,  ...,  0.0045,  0.0031,  0.0039]],\n",
       "         device='cuda:0', dtype=torch.bfloat16),\n",
       "  'token_id': 128257},\n",
       " 'bot_token': {'token': '###BOT###',\n",
       "  'replace_embedding_with': 'The assistant said:',\n",
       "  'new_embedding': tensor([[-0.0043, -0.0041, -0.0040,  ..., -0.0021, -0.0050,  0.0069]],\n",
       "         device='cuda:0', dtype=torch.bfloat16),\n",
       "  'token_id': 128258}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EXTRA_TOKENS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LqfIHZaFeL7e",
    "outputId": "e6e391d8-f87f-4751-86ff-fc676870c5a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "last_speaker\n",
       "assistant    56066\n",
       "human         2471\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do some EDA on our data\n",
    "text_pd = pd.concat([pd.DataFrame(dataset['train']), pd.DataFrame(dataset['test'])])\n",
    "\n",
    "text_pd['split'] = ['train'] * len(dataset['train']) + ['test'] * len(dataset['test'])\n",
    "text_pd['convo_length'] = text_pd['conversation'].apply(len)\n",
    "text_pd['last_speaker'] = text_pd['conversation'].apply(lambda x: x[-1][0])\n",
    "\n",
    "text_pd = text_pd[text_pd['last_speaker'].isin(['assistant', 'human'])]\n",
    "\n",
    "# WAY more assistants ending conversations than humans. We want those frankly.\n",
    "#  conversations where humans speak last aren't as useful\n",
    "text_pd['last_speaker'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "Lmx_O-BEAweT",
    "outputId": "3765b73c-4629-495b-f86f-bb16bc64c4b9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"text_pd\",\n  \"rows\": 58537,\n  \"fields\": [\n    {\n      \"column\": \"conversation\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"test\",\n          \"train\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"convo_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_speaker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"human\",\n          \"assistant\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "text_pd"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-1ffedfc9-5478-4800-8ecc-90e564e12e38\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conversation</th>\n",
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       "      <th>last_speaker</th>\n",
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       "      <td>[[human, Cuéntame un chiste sobre un tomate y ...</td>\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-1ffedfc9-5478-4800-8ecc-90e564e12e38 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-1ffedfc9-5478-4800-8ecc-90e564e12e38');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "\n",
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       "\n",
       "<style>\n",
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       "      --hover-bg-color: #E2EBFA;\n",
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       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
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       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
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       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
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       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "      border-right-color: var(--fill-color);\n",
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       "    60% {\n",
       "      border-color: transparent;\n",
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       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
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       "    }\n",
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       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-5541f6d2-3e24-433e-a4fc-c23c6fabe217 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                        conversation  split  convo_length  \\\n",
       "0  [[human, Cuéntame un chiste sobre un tomate y ...  train             2   \n",
       "1  [[human, Write a letter recommending someone f...  train             2   \n",
       "\n",
       "  last_speaker  \n",
       "0    assistant  \n",
       "1    assistant  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_pd.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K-u4g4M3CY_K"
   },
   "outputs": [],
   "source": [
    "# only keep convos ending with an assistant\n",
    "text_pd = text_pd[text_pd['last_speaker'] == 'assistant']\n",
    "text_pd = text_pd[text_pd['convo_length'] % 2 == 0]\n",
    "\n",
    "text_pd = text_pd.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "id": "8x_6yMT4A4TF",
    "outputId": "ebc72ec8-4c50-471a-9f03-16246d57a434"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='convo_length'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text_pd['convo_length'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Jq-k3yt5Suf5",
    "outputId": "a89c9b36-bb00-491b-e5a1-c8fd0c5d01d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first_question\n",
       "你好                                                                                                                             10\n",
       "Hola                                                                                                                            4\n",
       "Write a poem about the ocean.                                                                                                   4\n",
       "Write a short story about a haunted house.                                                                                      4\n",
       "Generate a list of 10 synonyms for the word \"happy.\"                                                                            4\n",
       "                                                                                                                               ..\n",
       "Generate a paragraph on the topic of gun control laws.                                                                          1\n",
       "Edit the given paragraph to add descriptive adjectives. \"The room was large and empty with white walls and a wooden floor.\"     1\n",
       "Find and replace all instances of the word \"happy\" with the word \"jolly\" in a document.                                         1\n",
       "Write a detailed description of A wise and experienced elder                                                                    1\n",
       "Find three synonyms for the word \"strong\".                                                                                      1\n",
       "Name: count, Length: 55465, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_pd['first_question'] = text_pd['conversation'].apply(lambda x: x[0][1])\n",
    "text_pd['first_question'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dbRVEH2r2lW1"
   },
   "source": [
    "# Filter out by language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LwQnLokHGL16"
   },
   "outputs": [],
   "source": [
    "FILTER_EN = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "8a904518a6b74066bfad2f0d8547fcf2",
      "390cc8722baa4a85b160a213ee67e638",
      "48ae69e9dcd94cd38cdfb0017d344a60",
      "b03a07143fef4002bfd8d2775fb6df91",
      "2b08c5f8836542c585fd6743597d3372",
      "8e436db037e2498784a72ffb89098daa",
      "f6391dba630648df93f16a9ebb00e8de",
      "2d3bc67f8f7d4daa99ffa321c87f469a",
      "907f8fa170c3415b89850b4f773abe51",
      "cd969f2b9a2a4361a8e6a3b2412d50a0",
      "9fec4cb4e617499797c22f4066a84159",
      "fb0547a37df0480a835ff884320b5872",
      "1537b98cba994caf89e162fd362617de",
      "c6824b9b1d234ed7ae0bdb1f782362d3",
      "3114e0520b0542d793690d293c29862a",
      "90a9354af0ef405d85bbfb306c1307ee",
      "d0a9e130d2dc4f5987b55630b79a6aa1",
      "96e76a8661dd4d41b78afb1df9ba873c",
      "a3c640613dc24cca96e76b15d5d6ce2c",
      "c21c5b415f9548cc9939afa115efc65f",
      "68bd434c9e5c4313bdd0147c7c4918ee",
      "60f6c225800743768b09a6efbbfeeec8"
     ]
    },
    "id": "22qh0FD5Tsvp",
    "outputId": "8d77569a-bfc5-45a4-ccfd-bb623b10eb59"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a904518a6b74066bfad2f0d8547fcf2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/137 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb0547a37df0480a835ff884320b5872",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/36 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Define a function for batch processing\n",
    "def detect_language_in_batches(batch, batch_size=128):\n",
    "    batch_results = pipe([b[0][1] for b in batch['conversation']])\n",
    "    return {'lang': [b['label'] for b in batch_results]}\n",
    "\n",
    "# Apply the function to the DataFrame\n",
    "if FILTER_EN:\n",
    "    pipe = pipeline(\"text-classification\", model=\"papluca/xlm-roberta-base-language-detection\", truncation=True, max_length=64)\n",
    "    dataset = dataset.map(detect_language_in_batches, batch_size=128, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 498
    },
    "id": "OTfEXSZ-RxyZ",
    "outputId": "6c598ad3-97a5-4cf7-b518-500d92aa21db"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if FILTER_EN:\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # Filtering for English language\n",
    "\n",
    "    # Counting the occurrences of each language\n",
    "    lang_counts = pd.Series(dataset['train']['lang']).value_counts()\n",
    "\n",
    "    # Creating the bar plot\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    lang_counts.plot.bar(color='teal', edgecolor='black')\n",
    "\n",
    "    # Adding titles and labels\n",
    "    plt.title('Frequency of Languages', fontsize=15)\n",
    "    plt.xlabel('Language', fontsize=12)\n",
    "    plt.ylabel('Count', fontsize=12)\n",
    "\n",
    "    # Adding grid for better readability\n",
    "    plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "    # Displaying the plot\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "snbcZIG1w3qn"
   },
   "outputs": [],
   "source": [
    "if FILTER_EN:\n",
    "    dataset = dataset.filter(lambda x: x['lang'] == 'en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lMyHzdUWNxOt"
   },
   "outputs": [],
   "source": [
    "def join_convo(conversation):\n",
    "    convo = ''''''\n",
    "    last_speaker = None\n",
    "    for speaker, message in conversation:\n",
    "        last_speaker = speaker\n",
    "        if speaker == 'human':\n",
    "            convo += f\"{EXTRA_TOKENS['human_token']['token']} {message} \"\n",
    "        elif speaker == 'assistant':\n",
    "            convo += f\"{EXTRA_TOKENS['bot_token']['token']} {message} \"\n",
    "    if last_speaker == 'human':\n",
    "        return convo.strip() + f\" {EXTRA_TOKENS['bot_token']['token']}\"\n",
    "    return convo.strip() + f\" {EXTRA_TOKENS['stop_token']['token']}\"\n",
    "\n",
    "text_pd['text'] = text_pd['conversation'].apply(join_convo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hz-lGvNLNxOt",
    "outputId": "813a81f1-58d5-4183-ca3e-81abc6e5feb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['human', 'Cuéntame un chiste sobre un tomate y algo más'],\n",
       " ['assistant',\n",
       "  'Esto es un tomate que está haciendo el ridículo y otro tomate le grita: TE ESTÁS PONIENDO COMO UN HUMANO!']]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train'][0]['conversation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Nkx6I_K8RGgr"
   },
   "outputs": [],
   "source": [
    "model.generation_config.do_sample = True\n",
    "model.config.use_cache = False\n",
    "# \"Setting pretraining_tp to a value different than 1 will activate the more accurate but slower computation of the linear layers, which should better match the original logits.\"\n",
    "model.config.pretraining_tp = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S0ke6TqgFu36",
    "outputId": "069539fd-1e43-4bb7-bf09-5936a5ef710c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###HUMAN### Who was the first president of the USA? ###BOT###\n"
     ]
    }
   ],
   "source": [
    "print(join_convo([['human', \"Who was the first president of the USA?\"]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7NoI2dXlRGgt"
   },
   "outputs": [],
   "source": [
    "# Load LoRA configuration to make training much more efficient\n",
    "peft_args = LoraConfig(\n",
    "    lora_alpha=32,\n",
    "    lora_dropout=0.05,\n",
    "    r=128,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "model = get_peft_model(model, peft_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kIRENIvl7w7B",
    "outputId": "7c96b9fd-b416-4217-ea75-dffb2eb31bb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 54,525,952 || all params: 8,084,811,776 || trainable%: 0.6744245074679646\n"
     ]
    }
   ],
   "source": [
    "model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aAIpv3tNRnHz"
   },
   "source": [
    "# Testing the model before SFT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145
    },
    "id": "6NHYn2KQMm0S",
    "outputId": "4ef38b82-442a-44e3-efb6-e4d9e51480e8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128256 for open-end generation.\n",
      "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1477: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'###HUMAN### Who was the first president of the USA? ###BOT### to by?  The name of the person is \"George\" and his last name is \"Washington\".  That person also ran the election for a president who was one years older than himself, who won him the voting by getting the absolute majority. \\n\"\"\"\\n\\n'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(\n",
    "    model.generate(\n",
    "        **tokenizer(join_convo([['human', \"Who was the first president of the USA?\"]]), return_tensors='pt'),\n",
    "        max_length=64,\n",
    "        eos_token_id=EXTRA_TOKENS['stop_token']['token_id']\n",
    "        )[0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145
    },
    "id": "hZbCnay_E_QH",
    "outputId": "7f895474-6d96-4e94-e24f-5c64b6c7426b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128256 for open-end generation.\n",
      "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1477: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Who was the first president of the USA? What role did he play in the American Revolution?\\nGeorge Washington. He was a great general and one of the greatest leaders of the revolution.\\nWho was the first female US president? What role did she play in the American Revolution?\\nAbigail Adams. She was a very important'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(\n",
    "    model.generate(\n",
    "        **tokenizer(\"Who was the first president of the USA?\", return_tensors='pt'),\n",
    "        max_length=64,\n",
    "        eos_token_id=EXTRA_TOKENS['stop_token']['token_id']\n",
    "        )[0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244,
     "referenced_widgets": [
      "ed48a040e56e487ab358c43fdd808136",
      "5251d253ac1b43e28b5d45fc49b8ba0c",
      "aaa010e3e1304ff5b2b83ae7bd23200f",
      "f54d56ed00f142e9bc97eacf993fbb12",
      "59997470461b47dc9d1c29693788fa3a",
      "5806a2430c624826a5f99189ca6547ca",
      "d69acca18c2e4fdfb5dfc475ea1a379b",
      "960448f923a346bfa3cc11edb4da6f31",
      "3f55ba327f7f4d888e31bd84affc589b",
      "da9776992f7a49859fd855e13ea6520a",
      "bb986b5172404a638fcb5ebe03657c61",
      "fab6c98410ac43feaf9b76e6b48ea2a0",
      "eb69a299f2db4aca84736b6c21bf86cd",
      "d1afa0f8597c427bb4aa315d7b2a7743",
      "b273e68ee141456596c0718729951770",
      "1b009dcb008d47838bdb09234f995f47",
      "969c467053814888bf4d3a5c896a9da4",
      "f1acd1457e7b4e8a8ce0cfcb0df1ae54",
      "a73e9f49513a4c3cab44407d05a57a9e",
      "343b9bd2b4734f0191ecb6d9c4b4cd97",
      "87c682d9e7c74848a13edc430434d301",
      "944966f0c7704952b663c436488f3b9c"
     ]
    },
    "id": "L0h9ERFgCXNx",
    "outputId": "80a77c8c-c250-4010-ba33-908c68e71206"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parameter 'function'=<function <lambda> at 0x7cb5d0b53d00> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n",
      "WARNING:datasets.fingerprint:Parameter 'function'=<function <lambda> at 0x7cb5d0b53d00> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed48a040e56e487ab358c43fdd808136",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/46837 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fab6c98410ac43feaf9b76e6b48ea2a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/11710 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'conversation': [['human', 'Cuéntame un chiste sobre un tomate y algo más'],\n",
       "  ['assistant',\n",
       "   'Esto es un tomate que está haciendo el ridículo y otro tomate le grita: TE ESTÁS PONIENDO COMO UN HUMANO!']],\n",
       " 'text': '###HUMAN### Cuéntame un chiste sobre un tomate y algo más ###BOT### Esto es un tomate que está haciendo el ridículo y otro tomate le grita: TE ESTÁS PONIENDO COMO UN HUMANO! ###STOP###'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.map(lambda x: {'text': join_convo(x['conversation'])})\n",
    "\n",
    "dataset['train'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "df4c0add00cc4e099079fc1a7fae0701",
      "1eab3b7673cb4e838c7428252a13e6e6",
      "40a60b82642c49648f92fd18af344e40",
      "94fb2be888c548ef843641776d1db28f",
      "1caf1e51c3544f698b46d31d11e58746",
      "891bfb6a65374262a59ef0220de48767",
      "71563e2cd55b48728f75aba39dac64ca",
      "164316c4320e48c6b713c69e9a52a497",
      "60587853f1c64e24aff043db932d9196",
      "f738f2f21619489fb2d87652fbafa882",
      "bc2243ff22274131b38b061f17d5e539",
      "ff89e214e2fc46db8cf87cd2f226e3f4",
      "9fdb6e26ad504fbf8715b181c72b03f5",
      "df8daf948ece489bbe8f9a1318c66748",
      "8dc87e4c06bb4387964bd260d1f922ab",
      "d0c2db169ac34d5da0dbd49bffd5b199",
      "049230e4b3464cd4bce0e17ad5f6cced",
      "a149f3d7509b4dc2a00991b73c66c59e",
      "cb8daf7afa9949fe8e5ab4d0bf2c0173",
      "74a987d2f4cc455682993cbcf25675d2",
      "ac300f6e421440f09be90dd3884e9f62",
      "10b036b09274412a9e7ec9842f65c7e5"
     ]
    },
    "id": "SnOHo12FJ2ye",
    "outputId": "74618c9a-df36-48da-ac14-cdb7863544d6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df4c0add00cc4e099079fc1a7fae0701",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/46837 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff89e214e2fc46db8cf87cd2f226e3f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/11710 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = dataset.map(lambda x: tokenizer(x['text']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xAfH84Q1798B"
   },
   "outputs": [],
   "source": [
    "# zero out loss before the final response token, only judge the model on the completion tokens (the final assistant message)\n",
    "class DataCollatorForCompletionOnlyLM(DataCollatorForLanguageModeling):\n",
    "\n",
    "    def torch_call(self, examples):\n",
    "        self.tokenizer.pad(examples, return_tensors=\"pt\", pad_to_multiple_of=self.pad_to_multiple_of)\n",
    "        batch = super().torch_call(examples)\n",
    "        response_token_id = EXTRA_TOKENS['bot_token']['token_id']\n",
    "00\n",
    "        labels = batch[\"input_ids\"].clone()\n",
    "        for i in range(len(examples)):\n",
    "            tokens = batch[\"input_ids\"].clone().tolist()[i]\n",
    "            if tokens.count(response_token_id) == 0:\n",
    "                print(tokens)\n",
    "            up_until = len(tokens) - 1 - tokens[::-1].index(response_token_id)\n",
    "            labels[i, :up_until + 1] = -100\n",
    "\n",
    "        batch[\"labels\"] = labels\n",
    "\n",
    "        return batch\n",
    "\n",
    "data_collator = DataCollatorForCompletionOnlyLM(\n",
    "    tokenizer=tokenizer, mlm=False, return_tensors=\"pt\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "id": "wTk3Ej3jNxOv",
    "outputId": "7685f1da-74be-4d91-8f25-35c8b02b2ebf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# token distribution\n",
    "pd.Series([len(x) for x in dataset['train']['input_ids']]).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 260,
     "referenced_widgets": [
      "82b4cdf55d6a434abf95f4125487d8ed",
      "8b49c7efcece446fa370de44a1b1aff9",
      "7b5b61752f7b48b797ba82f80947f9c2",
      "e6a721fc02064bbe983f72e2a8b2309e",
      "47013fcf9fe34897a7e87e890a425353",
      "84bcff9a13be4e5baa24284db1ea26a2",
      "686d3052deb64b228cd89443d4754261",
      "cbbdec3524d6494587282a85968546e9",
      "c462ad31a3864b419ac67f3ccb7eff4f",
      "1f44a3b46f914827a5ba7821f421c14b",
      "b9052c5ca31441cca770cf6b19fe84c5",
      "7c43ae2e0087493da74a0ca1829b5a53",
      "475247d988c84dbe9f92e055d6fe0ebd",
      "1cc432941129485f88886c5810feb7ba",
      "c9ad259c1e334e8eb701236de8d7436d",
      "eca657d097a74271a07fc5f6cdd71f44",
      "45a5fbaebcf245fba242bc548bbf0930",
      "b85de88d240a4f519fadd94225d3c08f",
      "232a585dd73d41ab9f0c138ba8891a51",
      "4aac40f9c5cc4b829a9c5155d1f93c9c",
      "b2413597466446789c19ef4015939717",
      "8c0c3dd815ed43e08275554786f1ef28"
     ]
    },
    "id": "q57odw6iNxOw",
    "outputId": "2908023a-95d3-46d5-8a2a-e0cb32de8048"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82b4cdf55d6a434abf95f4125487d8ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filter:   0%|          | 0/46837 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c43ae2e0087493da74a0ca1829b5a53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filter:   0%|          | 0/11710 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['conversation', 'text', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 46580\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['conversation', 'text', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 11649\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove elements that are too long for the LLM\n",
    "dataset = dataset.filter(\n",
    "    lambda x: len(x['input_ids']) < 1024  # truncating a lot for memory\n",
    "    )\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "id": "2poRf2-TD-aS",
    "outputId": "2623cb09-85f6-4976-d3d9-c075403593db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series([len(x) for x in dataset['train']['input_ids']]).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vRvhLdK20wyH",
    "outputId": "33a41569-6c2e-4a16-ba64-a080e44d9f37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "profoz\n"
     ]
    }
   ],
   "source": [
    "!huggingface-cli whoami"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N7RMb3UHRGgu",
    "outputId": "1993dcfd-289f-4d35-f945-c02e8461a0f6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
      "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Set supervised fine-tuning parameters\n",
    "training_params = TrainingArguments(\n",
    "    output_dir=\"./results\",\n",
    "    num_train_epochs=1,\n",
    "    per_device_train_batch_size=2,\n",
    "    per_device_eval_batch_size=4,\n",
    "    gradient_accumulation_steps=4,\n",
    "    logging_steps=5,\n",
    "    learning_rate=2e-4,\n",
    "    weight_decay=0.01,\n",
    "    fp16=False,\n",
    "    load_best_model_at_end=True,\n",
    "    evaluation_strategy='epoch',\n",
    "    save_strategy='epoch',\n",
    "    bf16=False,\n",
    "    warmup_ratio=0.1,\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "\n",
    "    push_to_hub=True,\n",
    "    hub_model_id=\"profoz/sawyer-llama-3\",\n",
    "    hub_strategy=\"every_save\",\n",
    ")\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset['train'],\n",
    "    eval_dataset=dataset['test'],\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_params,\n",
    "    data_collator=data_collator\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "NkqYHqW-0av8",
    "outputId": "7ab9e497-f324-4513-8a01-5a2a8002a2ab"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'profoz/sawyer-llama-3'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.hub_model_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9egMcxtPWsC7",
    "outputId": "1046c739-fd2a-46e2-c9af-a20703dabe29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.3733e-03,  5.0964e-03, -3.0365e-03,  ...,  2.2888e-03,\n",
       "         -1.9531e-03, -1.7166e-05],\n",
       "        [-2.7313e-03,  1.9379e-03, -1.3733e-03,  ..., -5.1498e-05,\n",
       "         -1.3962e-03, -1.9836e-03],\n",
       "        [ 9.5367e-04, -1.3367e-02,  4.1771e-04,  ...,  2.5940e-03,\n",
       "          7.0496e-03,  4.1809e-03],\n",
       "        ...,\n",
       "        [-1.9775e-02, -8.9722e-03,  5.1880e-03,  ...,  2.2461e-02,\n",
       "          1.0803e-02,  8.2397e-03],\n",
       "        [-4.0894e-03, -6.9580e-03, -5.3406e-03,  ...,  4.5166e-03,\n",
       "          3.0670e-03,  3.9062e-03],\n",
       "        [-4.3030e-03, -4.1199e-03, -4.0283e-03,  ..., -2.1057e-03,\n",
       "         -4.9744e-03,  6.8665e-03]], device='cuda:0', dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.base_model.model.model.embed_tokens.weight.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 214
    },
    "id": "P3IzO6n45pbL",
    "outputId": "7a6571b0-5e51-4574-b8d4-bda9083e7808"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='2913' max='2913' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [2913/2913 09:30]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mprofoz\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.16.6"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/content/wandb/run-20240419_160854-nkyfjdv5</code>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/profoz/huggingface/runs/nkyfjdv5' target=\"_blank\">earnest-firefly-264</a></strong> to <a href='https://wandb.ai/profoz/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/profoz/huggingface' target=\"_blank\">https://wandb.ai/profoz/huggingface</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/profoz/huggingface/runs/nkyfjdv5' target=\"_blank\">https://wandb.ai/profoz/huggingface/runs/nkyfjdv5</a>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 1.5043210983276367,\n",
       " 'eval_runtime': 570.6021,\n",
       " 'eval_samples_per_second': 20.415,\n",
       " 'eval_steps_per_second': 5.105}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "0Y99UKM_RGgu",
    "outputId": "8945bd23-8d34-47ce-ab08-2ea24757b63f",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='583' max='5822' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [ 583/5822 10:33 < 1:35:12, 0.92 it/s, Epoch 0.10/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
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       "<IPython.core.display.HTML object>"
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='5822' max='5822' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [5822/5822 1:55:33, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.994800</td>\n",
       "      <td>0.970850</td>\n",
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    {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='5826' max='2913' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [2913/2913 2:04:55]\n",
       "    </div>\n",
       "    "
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:168: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=5822, training_loss=0.9559917277341362, metrics={'train_runtime': 6935.0686, 'train_samples_per_second': 6.717, 'train_steps_per_second': 0.84, 'total_flos': 4.9022049588156826e+17, 'train_loss': 0.9559917277341362, 'epoch': 1.0})"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VvoymtqiWtHx",
    "outputId": "40f5fa76-35fc-4be9-bebc-f6ca81610d24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.3733e-03,  5.0964e-03, -3.0365e-03,  ...,  2.2888e-03,\n",
       "         -1.9531e-03, -1.7166e-05],\n",
       "        [-2.7313e-03,  1.9379e-03, -1.3733e-03,  ..., -5.1498e-05,\n",
       "         -1.3962e-03, -1.9836e-03],\n",
       "        [ 9.5367e-04, -1.3367e-02,  4.1771e-04,  ...,  2.5940e-03,\n",
       "          7.0496e-03,  4.1809e-03],\n",
       "        ...,\n",
       "        [-1.9775e-02, -8.9722e-03,  5.1880e-03,  ...,  2.2461e-02,\n",
       "          1.0803e-02,  8.2397e-03],\n",
       "        [-4.0894e-03, -6.9580e-03, -5.3406e-03,  ...,  4.5166e-03,\n",
       "          3.0670e-03,  3.9062e-03],\n",
       "        [-4.3030e-03, -4.1199e-03, -4.0283e-03,  ..., -2.1057e-03,\n",
       "         -4.9744e-03,  6.8665e-03]], device='cuda:0', dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.base_model.model.model.embed_tokens.weight.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qL5i358PRGgu",
    "outputId": "de51f837-7516-454d-83e4-ec622cf0f688"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:160: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Uncomment to save trained model locally\n",
    "\n",
    "# new_model = \"llama-ft\"\n",
    "# trainer.model.save_pretrained(new_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wvm8T-DPRwNS"
   },
   "source": [
    "# Testing SAWYER with SFT Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "vCTJ8o6Il3ay",
    "outputId": "67bc8460-462a-49cb-bc98-338a9180dddd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'###HUMAN### Who was the first president of the USA? ###BOT### George Washington. ###STOP###'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the model we just trained (not the one we are about to de-serialize)\n",
    "tokenizer.decode(\n",
    "    trainer.model.generate(\n",
    "        **tokenizer(join_convo([['human', \"Who was the first president of the USA?\"]]), return_tensors='pt').to(0),\n",
    "        max_length=64,\n",
    "        eos_token_id=EXTRA_TOKENS['stop_token']['token_id']\n",
    "        )[0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "hzOug7AG4-y6",
    "outputId": "96085b73-08b0-412e-8d34-51a2c9bcfa60"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'###HUMAN### hello ###BOT### Hello, how can I help you today? ###STOP###'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the model we just trained (not the one we are about to de-serialize)\n",
    "tokenizer.decode(\n",
    "    trainer.model.generate(\n",
    "        **tokenizer(join_convo([['human', \"hello\"]]), return_tensors='pt').to(0),\n",
    "        max_length=64,\n",
    "        eos_token_id=EXTRA_TOKENS['stop_token']['token_id']\n",
    "        )[0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mz4bUIAZeTdv",
    "outputId": "1ffaa0c4-2d0f-4acc-cae4-fe58fe288e49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for e, d in EXTRA_TOKENS.items():\n",
    "    print(d['new_embedding'].detach().cpu().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 126
    },
    "id": "qw89YzvdCbAl",
    "outputId": "cc319822-43d1-43c3-c0e0-964f4e9bfa93"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:168: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/profoz/sawyer-llama-3/commit/2b049cb9574c999392222faea9d13e17014c8e90', commit_message='End of training', commit_description='', oid='2b049cb9574c999392222faea9d13e17014c8e90', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.push_to_hub()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 120,
     "referenced_widgets": [
      "0e72bd202a6c428ea1ddffe5aee2a4f0",
      "37a37aef5da441d28a2e9739a6e614c2",
      "bf23c4e4630a4567a81115af35eb4b70",
      "80d6939334534de294fff550a269ec34",
      "6b5d19c3270b4bd4b310fdc2bbfcf57b",
      "8187d7bd4dab471bbc7dcdab622f861d",
      "18a98b9356ed46e4a257cf7542c4c526",
      "4c680e45e8cd495d8cd87cbc4e1a0742",
      "4a67be7ea7ae4514bff06f5317bc16b5",
      "a1ea64ad50a246e28be5818649c45820",
      "59f3066001c244968b573c46e5755b84"
     ]
    },
    "id": "bJIC0-8Z3Jfz",
    "outputId": "ca02f9bd-25dd-4d9a-c06d-2e7cd413d9f2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e72bd202a6c428ea1ddffe5aee2a4f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/1.43k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/profoz/sawyer-llama-3/commit/fca70aa9464eb4198e36a3420e7556a2399b8c3f', commit_message='Upload tokenizer', commit_description='', oid='fca70aa9464eb4198e36a3420e7556a2399b8c3f', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.push_to_hub(trainer.hub_model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HmmArmgZYCrg"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_hzzxvCYYDpf"
   },
   "source": [
    "# Reload model and merge it with LoRA weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
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    "id": "xZFl7XR4HmB_",
    "outputId": "b457df9c-f46e-4ed5-af7c-d14b31c1bfd5"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d9f48069c0741f387b8b33afe2147ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
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      },
      "text/plain": [
       "adapter_config.json:   0%|          | 0.00/653 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1f9524c5e2a47d9a1394b91be2078fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.safetensors:   0%|          | 0.00/2.32G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the base model for causal language modeling from the pretrained model specified by 'base_model'\n",
    "hf_load_model = AutoModelForCausalLM.from_pretrained(\n",
    "    base_model,\n",
    "    low_cpu_mem_usage=True,  # Optimize memory usage\n",
    "    return_dict=True,        # Ensure the model returns a dictionary\n",
    "    torch_dtype=torch.bfloat16,  # Use bfloat16 precision for faster computation\n",
    "    device_map={\"\": 0},      # Map the model to the first available GPU device\n",
    ")\n",
    "\n",
    "# Resize the token embeddings to match the tokenizer's vocabulary size\n",
    "hf_load_model.resize_token_embeddings(len(tokenizer))\n",
    "\n",
    "# Load the PeftModel from a pretrained model specified by 'trainer.hub_model_id'\n",
    "hf_load_model = PeftModel.from_pretrained(hf_load_model, trainer.hub_model_id)\n",
    "\n",
    "# Merge the model's weights. This is optional.\n",
    "hf_load_model = hf_load_model.merge_and_unload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-SSFtailVKC9",
    "outputId": "91ec1378-df6b-4a93-c500-52d4f295818e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'stop_token': {'token': '###STOP###',\n",
       "  'replace_embedding_with': 'stop talking',\n",
       "  'new_embedding': tensor([[-0.0198, -0.0090,  0.0052,  ...,  0.0225,  0.0108,  0.0082]],\n",
       "         device='cuda:0', dtype=torch.bfloat16),\n",
       "  'token_id': 128256},\n",
       " 'human_token': {'token': '###HUMAN###',\n",
       "  'replace_embedding_with': 'The human said:',\n",
       "  'new_embedding': tensor([[-0.0041, -0.0070, -0.0053,  ...,  0.0045,  0.0031,  0.0039]],\n",
       "         device='cuda:0', dtype=torch.bfloat16),\n",
       "  'token_id': 128257},\n",
       " 'bot_token': {'token': '###BOT###',\n",
       "  'replace_embedding_with': 'The assistant said:',\n",
       "  'new_embedding': tensor([[-0.0043, -0.0041, -0.0040,  ..., -0.0021, -0.0050,  0.0069]],\n",
       "         device='cuda:0', dtype=torch.bfloat16),\n",
       "  'token_id': 128258}}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EXTRA_TOKENS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vTQbM95WVLyh",
    "outputId": "a3f8bc42-3a9b-4f4e-9664-8b4e315f7ec7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        ...,\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True]], device='cuda:0')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.model.model.embed_tokens.weight.data == hf_load_model.model.embed_tokens.weight.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n-napedjVPsA",
    "outputId": "49f1b8e2-a2ef-4ac6-d879-40e3d5d8eb9d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.0198, -0.0090,  0.0052,  ...,  0.0225,  0.0108,  0.0082],\n",
       "       device='cuda:0', dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.model.model.embed_tokens.weight.data[128256]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f5lIbxuDVPqE",
    "outputId": "8d7834e1-6f50-4e6c-8d97-dbf7e8913f3f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.0198, -0.0090,  0.0052,  ...,  0.0225,  0.0108,  0.0082],\n",
       "       device='cuda:0', dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf_load_model.model.embed_tokens.weight.data[128256]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wUFKTonnVPn5",
    "outputId": "39c326c9-61fc-4d49-b418-cfce60616d62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128256 tensor([[-0.0198, -0.0090,  0.0052,  ...,  0.0225,  0.0108,  0.0082]],\n",
      "       device='cuda:0', dtype=torch.bfloat16)\n",
      "128257 tensor([[-0.0041, -0.0070, -0.0053,  ...,  0.0045,  0.0031,  0.0039]],\n",
      "       device='cuda:0', dtype=torch.bfloat16)\n",
      "128258 tensor([[-0.0043, -0.0041, -0.0040,  ..., -0.0021, -0.0050,  0.0069]],\n",
      "       device='cuda:0', dtype=torch.bfloat16)\n"
     ]
    }
   ],
   "source": [
    "for extra_token, token_info in EXTRA_TOKENS.items():\n",
    "    print(token_info['token_id'], token_info['new_embedding'].clone().to(torch.bfloat16)[:5])\n",
    "    # hf_load_model.model.embed_tokens.weight.data[token_info['token_id']] = token_info['new_embedding'].clone().to(torch.bfloat16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IdoK5HkmVPhL",
    "outputId": "dd12490e-78e5-4569-f654-b625b9a59ccc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.0198, -0.0090,  0.0052,  ...,  0.0225,  0.0108,  0.0082],\n",
       "       device='cuda:0', dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf_load_model.model.embed_tokens.weight.data[128256]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QDu4w5NUBsYF",
    "outputId": "de901255-1d14-42ad-dfd3-64582101f1b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.0198, -0.0090,  0.0052,  ...,  0.0225,  0.0108,  0.0082],\n",
       "       device='cuda:0', dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf_load_model.model.embed_tokens.weight.data[128256]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a41u8uj8VUlm",
    "outputId": "cc7ca8bb-526e-4b36-80b7-86b523db2cd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        ...,\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True]], device='cuda:0')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.model.model.embed_tokens.weight.data == hf_load_model.model.embed_tokens.weight.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "Gd5RbNb72MvG",
    "outputId": "2f336efe-d894-41c1-ad80-691e9c57b12e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'###HUMAN### Who was the first president of the USA? ###BOT### George Washington. ###STOP###'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the model we just trained (not the one we are about to de-serialize)\n",
    "tokenizer.decode(\n",
    "    hf_load_model.generate(\n",
    "        **tokenizer(join_convo([['human', \"Who was the first president of the USA?\"]]), return_tensors='pt').to(0),\n",
    "        max_length=64,\n",
    "        eos_token_id=EXTRA_TOKENS['stop_token']['token_id']\n",
    "        )[0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qYN8Brupj9bL"
   },
   "outputs": [],
   "source": [
    "def generate_text(conversation, model, **kwargs):\n",
    "    prompt = join_convo(conversation)\n",
    "    return tokenizer.decode(\n",
    "        model.generate(\n",
    "            **tokenizer(prompt, return_tensors='pt').to(model.device),\n",
    "            max_length=128,\n",
    "            eos_token_id=EXTRA_TOKENS['stop_token']['token_id'],\n",
    "            **kwargs\n",
    "            )[0],\n",
    "        skip_special_tokens=True,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hu3t-Ibfrly2",
    "outputId": "0e43cf83-dabd-4f37-a7dc-fca25ca7b155"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###HUMAN### Who was the first president of the USA? ###BOT### George Washington. ###STOP###\n"
     ]
    }
   ],
   "source": [
    "# we want this to be about the same.\n",
    "print(generate_text(\n",
    "    [['human', \"Who was the first president of the USA?\"]],\n",
    "    hf_load_model\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RUj5-BS3Ac3v",
    "outputId": "ef27220e-ee36-457e-dbf2-c65fa7c6f92c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###HUMAN### Hey there ###BOT### Hello! How can I help you today? ###STOP###\n"
     ]
    }
   ],
   "source": [
    "# we want this to be about the same.\n",
    "print(generate_text(\n",
    "    [['human', \"Hey there\"]],\n",
    "    hf_load_model\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mW0hsdKEjm_J"
   },
   "outputs": [],
   "source": []
  }
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